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Sven Degroeve
Sven Degroeve
staff scientist, VIB
Zweryfikowany adres z vib-ugent.be
Tytuł
Cytowane przez
Cytowane przez
Rok
The Genome of Black Cottonwood, Populus trichocarpa (Torr. & Gray)
GA Tuskan, S Difazio, S Jansson, J Bohlmann, I Grigoriev, U Hellsten, ...
science 313 (5793), 1596-1604, 2006
45142006
Genome analysis of the smallest free-living eukaryote Ostreococcus tauri unveils many unique features
E Derelle, C Ferraz, S Rombauts, P Rouzé, AZ Worden, S Robbens, ...
Proceedings of the National Academy of Sciences 103 (31), 11647-11652, 2006
9042006
Random forests as a tool for ecohydrological distribution modelling
J Peters, B De Baets, NEC Verhoest, R Samson, S Degroeve, ...
ecological modelling 207 (2-4), 304-318, 2007
3462007
Feature subset selection for splice site prediction
S Degroeve, B De Baets, Y Van de Peer, P Rouzé
Bioinformatics 18 (suppl_2), S75-S83, 2002
1572002
Large-scale structural analysis of the core promoter in mammalian and plant genomes
K Florquin, Y Saeys, S Degroeve, P Rouze, Y Van de Peer
Nucleic acids research 33 (13), 4255-4264, 2005
1322005
SpliceMachine: predicting splice sites from high-dimensional local context representations
S Degroeve, Y Saeys, B De Baets, P Rouzé, Y Van de Peer
Bioinformatics 21 (8), 1332-1338, 2005
1302005
MS2PIP: a tool for MS/MS peak intensity prediction
S Degroeve, L Martens
Bioinformatics 29 (24), 3199-3203, 2013
1032013
Feature selection for splice site prediction: a new method using EDA-based feature ranking
Y Saeys, S Degroeve, D Aeyels, P Rouzé, Y Van de Peer
BMC bioinformatics 5 (1), 1-11, 2004
872004
Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction
Y Saeys, S Degroeve, D Aeyels, Y Van de Peer, P Rouzé
Bioinformatics 19 (suppl_2), ii179-ii188, 2003
752003
Bioinformatics Analysis of a Saccharomyces cerevisiae N-Terminal Proteome Provides Evidence of Alternative Translation Initiation and Post-Translational N …
K Helsens, P Van Damme, S Degroeve, L Martens, T Arnesen, ...
Journal of proteome research 10 (8), 3578-3589, 2011
702011
Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques
R Gabriels, L Martens, S Degroeve
Nucleic acids research 47 (W1), W295-W299, 2019
692019
Analysis of the resolution limitations of peptide identification algorithms
N Colaert, S Degroeve, K Helsens, L Martens
Journal of proteome research 10 (12), 5555-5561, 2011
632011
MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation
S Degroeve, D Maddelein, L Martens
Nucleic acids research 43 (W1), W326-W330, 2015
612015
Translation initiation site prediction on a genomic scale: beauty in simplicity
Y Saeys, T Abeel, S Degroeve, Y Van de Peer
Bioinformatics 23 (13), i418-i423, 2007
592007
Machine learning applications in proteomics research: how the past can boost the future
P Kelchtermans, W Bittremieux, K De Grave, S Degroeve, J Ramon, ...
Proteomics 14 (4-5), 353-366, 2014
582014
Predicting tryptic cleavage from proteomics data using decision tree ensembles
T Fannes, E Vandermarliere, L Schietgat, S Degroeve, L Martens, ...
Journal of proteome research 12 (5), 2253-2259, 2013
492013
DeepLC can predict retention times for peptides that carry as-yet unseen modifications
R Bouwmeester, R Gabriels, N Hulstaert, L Martens, S Degroeve
Nature Methods 18 (11), 1363-1369, 2021
462021
An algorithm for detecting and labeling drum events in polyphonic music
K Tanghe, S Degroeve, B De Baets
Proceedings of the 1st Annual Music Information Retrieval Evaluation …, 2005
462005
Proteome-derived peptide libraries to study the substrate specificity profiles of carboxypeptidases
S Tanco, J Lorenzo, J Garcia-Pardo, S Degroeve, L Martens, FX Aviles, ...
Molecular & Cellular Proteomics 12 (8), 2096-2110, 2013
452013
Comprehensive and empirical evaluation of machine learning algorithms for small molecule LC retention time prediction
R Bouwmeester, L Martens, S Degroeve
Analytical chemistry 91 (5), 3694-3703, 2019
432019
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